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DBMS > Drizzle vs. Fujitsu Enterprise Postgres vs. Heroic vs. Kinetica vs. Sphinx

System Properties Comparison Drizzle vs. Fujitsu Enterprise Postgres vs. Heroic vs. Kinetica vs. Sphinx

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonHeroic  Xexclude from comparisonKinetica  Xexclude from comparisonSphinx  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchFully vectorized database across both GPUs and CPUsOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSRelational DBMSTime Series DBMSRelational DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websitewww.postgresql.fastware.comgithub.com/­spotify/­heroicwww.kinetica.comsphinxsearch.com
Technical documentationwww.postgresql.fastware.com/­product-manualsspotify.github.io/­heroicdocs.kinetica.comsphinxsearch.com/­docs
DeveloperDrizzle project, originally started by Brian AkerPostgreSQL Global Development Group, Fujitsu Australia Software TechnologySpotifyKineticaSphinx Technologies Inc.
Initial release2008201420122001
Current release7.2.4, September 2012Fujitsu Enterprise Postgres 14, January 20227.1, August 20213.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache 2.0commercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++CJavaC, C++C++
Server operating systemsFreeBSD
Linux
OS X
Linux
Windows
LinuxFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyesno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesyesyesyes infovia Elasticsearchyesyes infofull-text index on all search fields
SQL infoSupport of SQLyes infowith proprietary extensionsyesnoSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
HQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
ODBC
RESTful HTTP API
Proprietary protocol
Supported programming languagesC
C++
Java
PHP
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Java
JavaScript (Node.js)
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnouser defined functionsnouser defined functionsno
Triggersno infohooks for callbacks inside the server can be used.yesnoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingpartitioning by range, list and by hashShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAM
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPfine grained access rights according to SQL-standardAccess rights for users and roles on table levelno

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More resources
DrizzleFujitsu Enterprise PostgresHeroicKineticaSphinx
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